




"""simple docstring"""







from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE_	     =       200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
SCREAMING_SNAKE_CASE_	     =       50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
SCREAMING_SNAKE_CASE_	     =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	len([g for position, g in enumerate(_lowercase    ) if g == main_target[position]]    )
    return (item, float(_lowercase    ))





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	random.randint(0			,len(_lowercase    ) - 1    )
    UpperCamelCase              =	parent_a[:random_slice] + parent_a[random_slice:]
    UpperCamelCase              =	parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	list(_lowercase    )
    if random.uniform(0			,1    ) < MUTATION_PROBABILITY:
        UpperCamelCase              =	random.choice(_lowercase    )
    return "".join(_lowercase    )





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,):


    """simple docstring"""







    UpperCamelCase              =	[]
    # Generate more children proportionally to the fitness score.
    UpperCamelCase              =	int(parent_a[1] * 100    ) + 1
    UpperCamelCase              =	10 if child_n >= 10 else child_n
    for _ in range(_lowercase    ):
        UpperCamelCase              =	population_score[random.randint(0			,_lowercase    )][0]

        UpperCamelCase       ,	UpperCamelCase              =	crossover(parent_a[0]			,_lowercase    )
        # Append new string to the population list.
        pop.append(mutate(_lowercase			,_lowercase    )    )
        pop.append(mutate(_lowercase			,_lowercase    )    )
    return pop





def      __snake_case (				_lowercase			,_lowercase			,_lowercase = True    ):


    """simple docstring"""







    if N_POPULATION < N_SELECTED:
        UpperCamelCase              =	f'{N_POPULATION} must be bigger than {N_SELECTED}'
        raise ValueError(_lowercase    )
    # Verify that the target contains no genes besides the ones inside genes variable.
    UpperCamelCase              =	sorted({c for c in target if c not in genes}    )
    if not_in_genes_list:
        UpperCamelCase              =	f'{not_in_genes_list} is not in genes list, evolution cannot converge'
        raise ValueError(_lowercase    )

    # Generate random starting population.
    UpperCamelCase              =	[]
    for _ in range(_lowercase    ):
        population.append(''''''.join([random.choice(_lowercase    ) for i in range(len(_lowercase    )    )]    )    )

    # Just some logs to know what the algorithms is doing.
    UpperCamelCase       ,	UpperCamelCase              =	0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(_lowercase    )

        # Random population created. Now it's time to evaluate.

        # Adding a bit of concurrency can make everything faster,
        #
        # import concurrent.futures
        # population_score: list[tuple[str, float]] = []
        # with concurrent.futures.ThreadPoolExecutor(
        #                                   max_workers=NUM_WORKERS) as executor:
        #     futures = {executor.submit(evaluate, item) for item in population}
        #     concurrent.futures.wait(futures)
        #     population_score = [item.result() for item in futures]
        #
        # but with a simple algorithm like this, it will probably be slower.
        # We just need to call evaluate for every item inside the population.
        UpperCamelCase              =	[evaluate(_lowercase			,_lowercase    ) for item in population]

        # Check if there is a matching evolution.
        UpperCamelCase              =	sorted(_lowercase			,key=lambda _lowercase    : x[1]			,reverse=_lowercase    )
        if population_score[0][0] == target:
            return (generation, total_population, population_score[0][0])

        # Print the best result every 10 generation.
        # Just to know that the algorithm is working.
        if debug and generation % 10 == 0:
            print(
                f'\nGeneration: {generation}'
                f'\nTotal Population:{total_population}'
                f'\nBest score: {population_score[0][1]}'
                f'\nBest string: {population_score[0][0]}'    )

        # Flush the old population, keeping some of the best evolutions.
        # Keeping this avoid regression of evolution.
        UpperCamelCase              =	population[: int(N_POPULATION / 3    )]
        population.clear()
        population.extend(_lowercase    )
        # Normalize population score to be between 0 and 1.
        UpperCamelCase              =	[
            (item, score / len(_lowercase    )) for item, score in population_score
        ]

        # This is selection
        for i in range(_lowercase    ):
            population.extend(select(population_score[int(_lowercase    )]			,_lowercase			,_lowercase    )    )
            # Check if the population has already reached the maximum value and if so,
            # break the cycle.  If this check is disabled, the algorithm will take
            # forever to compute large strings, but will also calculate small strings in
            # a far fewer generations.
            if len(_lowercase    ) > N_POPULATION:
                break


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_	     =       (
      'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
  )
  SCREAMING_SNAKE_CASE_	     =       list(
      ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
      'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
  )
  SCREAMING_SNAKE_CASE_     ,    SCREAMING_SNAKE_CASE_     ,    SCREAMING_SNAKE_CASE_	     =       basic(target_str, genes_list)
  print(
      f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
  )


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


